CIO Business Intelligence

DECEMBER 7, 2023

Over the last year, generative AI—a form of artificial intelligence that can compose original text, images, computer code, and other content—has gone from experimental curiosity to a tech revolution that could be one of the biggest business disruptors of our generation. Likewise, they realize that human talent will be central to success.

Insurance 64

Insurance Experimentation Finance Data Governance 64

Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management. What Is Model Risk?

Risk Management 64

Risk Management Risk Modeling Enterprise 64

O'Reilly on Data

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 362

Marketing Experimentation Metrics Testing 362

Cloudera

OCTOBER 18, 2023

In the ever-evolving landscape of the financial services Industry, change is a constant and transformation is a requirement — to stay at pace with new regulations, risk mitigation, and the technological developments that support transformation.

Cost-Benefit 75

Cost-Benefit Testing Machine Learning Optimization 75

DataRobot Blog

NOVEMBER 15, 2022

At the event, a financial services panel discussion shared why iteration and experimentation are critical in an AI-driven data science environment. The panel discussion focused on Boyd’s Law of Iteration—a theory from dogfighting (military aviation strategy) which believes that the speed of iteration beats the quality of iteration.

Big Data 52

Big Data Experimentation Data-driven Machine Learning 52

Smarten

MAY 17, 2023

Whether you are looking at ChatGPT or Open AI and wondering how it might be applied to your business environment, it is important to understand the current state of this technology, the inherent risks and the possible opportunities. Creating Summary Data for Unstructured Documents (PDFs, HTML, Websites, etc.)

Digital Transformation 52

Digital Transformation Experimentation Risk Testing 52

CIO Business Intelligence

JULY 28, 2023

In addition to bottom-line benefits, employees are often inspired and motivated by innovation – seeking job opportunities that encourage experimentation and embrace new ideas. CIOs can also help draw a line of sight between the work the team is doing and the strategies of the company that enable it to win in the marketplace.

IT 97

IT Experimentation Digital Transformation Marketing 97

CIO Business Intelligence

JULY 3, 2023

The 2023 State of the CIO survey reveals that 71% of CIO respondents anticipate greater involvement in business strategy over the next three years with 85% stating they were becoming more digital- and innovation-focused. These models can extract meaning from digital data at scale and speed beyond the capabilities of human analysts.

Management 104

Management Experimentation Modeling Enterprise 104

CIO Business Intelligence

OCTOBER 18, 2023

But if there are any stop signs ahead regarding risks and regulations around generative AI, most enterprise CIOs are blowing past them, with plans to deploy an abundance of gen AI applications within the next two years if not already. A recent survey of nearly 1,000 IT decision-makers conducted by Foundry underscores this. “As

Risk 136

Risk Manufacturing Enterprise Technology 136

CIO Business Intelligence

AUGUST 17, 2023

But, as with any big new wave, there is a risk of once-promising projects being washed up and there are clear and obvious concerns over governance, quality and security. Generative AI is a key part of our business strategy, facilitating growth with AI-enabled processes already live in production,” he says.

Risk 93

Risk Cost-Benefit Strategy Experimentation 93

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. Code (62%) : Gen AI helps developers write code more efficiently and with fewer errors.

Cost-Benefit 93

Cost-Benefit Modeling Manufacturing Interactive 93

Ontotext

NOVEMBER 2, 2023

This shift from relational to graph approach has been well-documented by Gartner who advise that “using graph techniques at scale will form the foundation of modern data and analytics” and “graph technologies will be used in 80% of data and analytics innovations by 2025.”

IT 69

IT Cost-Benefit Data-driven Technology 69

CIO Business Intelligence

APRIL 24, 2024

Then the CIO can use that knowledge to ensure the success of technology initiatives that support business strategy, and then continue to expand their knowledge of finance and business from IT to the company as a whole. Developing and envisioning an AI-driven strategy is absolutely part of the equation,” he says.

Sales 125

Sales Finance Experimentation Cost-Benefit 125

CIO Business Intelligence

MARCH 5, 2024

Many other platforms, such as Coveo’s Relative Generative Answering , Quickbase AI , and LaunchDarkly’s Product Experimentation , have embedded virtual assistant capabilities but don’t brand them copilots. Today, top AI-assistant capabilities delivering results include generating code, test cases, and documentation.

Cost-Benefit 116

Cost-Benefit Advertising Experimentation Software 116

Corinium

APRIL 25, 2019

Most of my days focus on understanding what’s happening in the market, defining overall product strategy and direction, and translating into execution across the various teams. Life insurance needs accurate data on consumer health, age and other metrics of risk. Now, there is a data risk here. And then there is the Cloud.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

CIO Business Intelligence

OCTOBER 4, 2023

“Generative AI is just more far-reaching than traditional AI or machine learning, so the opportunities for disasters have grown,” says Bret Greenstein, partner and leader of the gen AI go-to-market strategy at PricewaterhouseCoopers. Then it’s time to assemble the final document. Often, generative AI will not be a good fit.

Cost-Benefit 138

Cost-Benefit Manufacturing Machine Learning Experimentation 138

IBM Big Data Hub

OCTOBER 20, 2023

Automated development: With AutoAI , beginners can quickly get started and more advanced data scientists can accelerate experimentation in AI development. Data extraction: Platform capabilities help sort through complex details and quickly pull the necessary information from large documents.

Machine Learning 76

Machine Learning Manufacturing Cost-Benefit Deep Learning 76

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. This fact is well documented by Kang & Schafer (2007). In observational studies treatment is assigned by nature, therefore its mechanism is unknown and needs to be estimated.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Cloudera

NOVEMBER 25, 2020

For example, it includes patient samples (blood, blood pressure, temperature, and more), patient information (age, gender, where they have lived, family situation, and other details), and treatment history, most of which is currently found only in paper documents.

Data Warehouse 57

Data Warehouse Unstructured Data Analytics Visualization 57

Domino Data Lab

JULY 2, 2019

People look toward online resources such as StackOverflow to find out how to use APIs when the documentation doesn’t have an example that fits. several aspects of that earlier U Washington project seem remarkably similar, including the experimental design, train/test data source, and even the slides.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

CIO Business Intelligence

SEPTEMBER 27, 2023

By suggesting a number of use cases, we further encourage experimentation and creative application.” All of this is a byproduct of training the model against implementation guides, knowledge bases, and other internal documentation that is refined against customer questions and responses, Lloyd says.

Strategy 124

Strategy Risk Data-driven Modeling 124

CIO Business Intelligence

APRIL 15, 2024

No one wants to implement AI that has a high risk of bias,” he says. “We Given the speed required, Lowden established a specialized team for the project to encourage a culture of experimentation and “moving fast to learn fast.” “You We were careful to lay out governance and controls around those things. We don’t write all the code.

Testing 80

Testing Experimentation Data Quality Machine Learning 80

CIO Business Intelligence

MAY 20, 2024

Nevertheless, the ongoing challenge of adapting to the strategies of terrorists and rogue states persists, especially as the volume of data flooding military intelligence capabilities has surged. The study documents “substantial returns to face-to-face meetings … (and) returns to serendipity.”

Management 100

Management Interactive Measurement Marketing 100

CIO Business Intelligence

AUGUST 1, 2023

As many CIOs prepare their 2024 budgets and digital transformation priorities, developing a strategy that seeks opportunities to evolve business models, targets near-term operational impacts, prioritizes where employees should experiment, and defines AI-related risk-mitigating plans is imperative.

Digital Transformation 98

Digital Transformation Unstructured Data Strategy Experimentation 98

Domino Data Lab

AUGUST 26, 2019

To support the iterative and experimental nature of industry work, Domino reached out to Addison-Wesley Professional (AWP) for appropriate permissions to excerpt the “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Why do we care? We could try lots of different values.

Testing 79

Testing Modeling Machine Learning Metrics 79